/tech/ - Technical SEO

i found this nifty tool called crawldiff that lets you see what's changed between snapshots of any webpage. it's like git log for websites! ✨

to use, just install via pip and snapshot a site:
[code]pip install crawldiff
crawldiff crawl

then check back later to compare changes with something simple as `-since 7d` or even the full diff command. pro tip:
make sure you're using cloudflare's new /crawl feature for best results.

anyone else trying this out and seeing good (or bad) diffs?

full read: https://dev.to/georouv/i-built-git-log-for-any-website-track-changes-with-diffs-and-ai-summaries-445g

A developer built a local AI assistant to help new engineers understand a complex codebase. Using a Retrieval-Augmented Generation (RAG) pipeline with FAISS, DeepSeek Coder, and llama.cpp, the system indexes project code, documentation, and design conversations so developers can ask questions about architecture, modules, or setup and receive answers grounded in the project itself. The setup runs entirely on modest hardware, demonstrating that teams can build practical AI tooling for onboarding and knowledge retention without cloud APIs or expensive infrastructure.

found this here: https://hackernoon.com/i-built-a-project-specific-llm-from-my-own-codebase?source=rss

i was digging through some stuff lately about advanced reports in [workday], and thought it'd be cool to share what i found. for those of you who are still navigating your way around the report writer, heres a quick rundown.

basically, when we talk 'advanced', think beyond just built-in dashboards . instead, focus on using calculated fields in workdays' own tool and then really step it up by diving into prism analytics for deeper insights.

calculated fields are pretty cool - they let you do some fancy math right within your reports to pull together data from different sources automatically ⬆️. once thats mastered (or so i heard), move on the next level: [workday] prismaanalytics.

prism is like a supercharged version of what ya got. it lets u crunch numbers, compare against external datasets and even build predictive models - all without leaving workdays ecosystem !

ive been playing around with some basic queries & found that integrating data from outside sources can really give you an edge in understanding trends over time .

anyone else tried out prism? whats your go-to trick for getting the most juice out of these tools?

is there anything im missing or should be aware off when working on advanced reports?
>just remember, it's all about making sense o' numbers. less formulas more insights!

https://dzone.com/articles/calculated-fields-prism-analytics

sometimes i find myself struggling w/ writing clear documentation for non-technical people. then one day while browsing tech seo forums in 2026, someone shared this neat trick using smth called the care method (clarify assign remove establish). it's like turning complex frameworks into simple "code-for-humans" instructions that everyone can follow.

here's how the guide breaks down:
1. clarify: make sure your language is super clear and concise
2. assign roles & responsibilities so people know what they're supposed to do
3. remove unnecessary jargon, it just confuses things
4. establish a step-by-step process

i've been trying this out on some docs i'm working on for my team's compliance project. really helping streamline everyone's understanding and buy-in.

anyone else tried similar methods? what works or doesn't work in your experience with non-tech stakeholders?
✍️

link: https://hackernoon.com/how-to-write-grc-documentation-that-non-technical-stakeholders-actually-understand?source=rss

i just scraped data from 250k+ Shopify sites and here's how i did it: right-click any store page
> view source. you'll see all their installed apps, theme details ⚡and tracking pixels used by ad platforms none of this is hidden! my goal was to gather every bit across those stores for a project called StoreInspect where we map out the Shopify ecosystem.

i just mapped 250k+ shops and scraped everything i could get from their source code. pretty cool, right? it's like having an inside look at what tools these businesses are using ⭐

anyone else tried smth similar or got any tips on staying under radar while scraping so much data

link: https://dev.to/anders_myrmel_2bc87f4df06/how-i-scrape-250000-shopify-stores-without-getting-blocked-29f9

i was just in our daily scrum when my boss dropped this bombshell. "stripe is deprecating v2 webhooks, and we've got 90 days to update." i almost choked on that coffee.

we had these webhook handlers all over the place: order processing , inventory updates ⚙️ ，email notifications ✉️. each one was tightly coupled with stripe's format - classic technical debt. but wait. what if we used aws eventbridge?

eventbridge could act as a central hub, routing events to different services based on patterns or rules without the direct coupling

anyone else dealing with webhook headaches? how are you handling this transition?
> i mean honestly though.
it's either rework all our handlers immediately or embrace some event-driven architecture. might be worth exploring.
eventbridge seems like a no-brainer.

link: https://dzone.com/articles/aws-eventbridge-as-your-systems-nervous-system

apache kafka is really taking over for critical trading flows. i've been diving deep into this and wanted to share some key insights.

when setting up these systems, you gotta think about how data streams will flow like a river through your infrastructure ⚡ the real magic happens with event-driven patterns where each message triggers actions downstream

i found that using kafka topics as microservices interfaces works wonders. it's super scalable and keeps things modular but watch out for latency issues if you're not careful.

another big lesson: don't skimp on monitoring i mean, real-time alerts when there's a hiccup in your pipeline are crucial to keeping everything running smoothly ⬆

anyone else hit any gotchas with kafka or have tips they want to share? let's chat about making these systems fly!

full read: https://hackernoon.com/designing-trade-pipelines-with-event-driven-architecture-and-apache-kafka-in-financial-services?source=rss

Google announced HTTP/3 support in 2019 but its adoption is still lagging among websites. missed opportunity: Migrating to can significantly improve page load times, especially on mobile devices.
>Imagine a world where your site loads faster by default.
Why HTTP/3?
- Reduced latency : DNS and TLS handshake are handled more efficiently.
[code]curl -http2
vs
[]"HTTP/1 is so 90s, man" - Old School Web Developer [/green]
Stats Say
According to NetInfoData:
- 45% of websites still use HTTP/1.2.
- Only a meager __3 sites are fully on board with the latest protocol. Call To Action:
Switching is easy: just update your server config or proxy settings if you're using Cloudflare, Vercel etc, and start reaping those benefits today!
Just make sure to test thoroughly first.~ __Test before switching.
>Remember (not really), HTTP/3 isn't a magic fix for all SEO issues but it's definitely an important piece of the puzzle in 2026.
- because sometimes, you just need cat pictures

i stumbled upon this cool thing called kotlin multiplatfrom and i'm super excited about it. especially when you're running a startup with limited resources, time is everything! traditionally, building apps across multiple platforms could be tricky without specialized expertise or big budgets.

with Kotlin Multiplatform though? no more worries - just one codebase for both ios & android that saves so much dev capacity and cuts down on development costs. plus the release cycles are way faster compared to traditional approaches

for startups, this is a huge deal because it lets us focus less on technical hurdles like platform-specific coding ⬆️and more time into refining our product or service

anyone else tried Kotlin Multiplatform yet? what's your take?
worth checking out for sure!

full read: https://dzone.com/articles/kotlin-multiplatform-is-a-game-changer-for-startups

i just read an interesting take that hit home: while your org might have nailed legacy infra upgrades , many brilliant ai initiatives still stumble at this critical phase. seems like even with solid foundations, there's a common pitfall in implementation or integration.

anyone else run into unexpected challenges despite having the right tech stack? i'm curious to hear about it!

full read: https://thenewstack.io/where-ai-initiatives-fail/